SETUP
# RESULTS for report
# 27.03.2021
# Load packages
library(summarytools)
## Registered S3 method overwritten by 'pryr':
## method from
## print.bytes Rcpp
## For best results, restart R session and update pander using devtools:: or remotes::install_github('rapporter/pander')
library(knitr)
library(kableExtra)
library(sjPlot)
library(sjstats)
library(reshape2)
library(dplyr)
##
## Attaching package: 'dplyr'
## The following object is masked from 'package:kableExtra':
##
## group_rows
## The following objects are masked from 'package:stats':
##
## filter, lag
## The following objects are masked from 'package:base':
##
## intersect, setdiff, setequal, union
library(ggplot2)
library(scales)
library(questionr)
##
## Attaching package: 'questionr'
## The following object is masked from 'package:sjstats':
##
## prop
## The following object is masked from 'package:summarytools':
##
## freq
library(shiny)
# load utility functions
source("./R/utilities.R")
## Load data and specify inclusion/exclusion
# read data 2004-2018
hse_combined = read.csv("./data/HSE_combined/hse_combined.csv")
# recode imd
hse_combined$imd <- as.factor(hse_combined$imd)
levels(hse_combined$imd) = c(
"Least deprived",
"Less deprived",
"Median deprived",
"More deprieved",
"Most deprived"
)
# EXCLUDE: < 16 years
hse_combined <- hse_combined[hse_combined$age5 >= 16,]
DATA SET DESCRIPTION
CLICK TO EXPAND
## Data set description
# Data rows: overall
nrow(hse_combined)
## [1] 149596
# Data rows: by year
kbl <- by(hse_combined,hse_combined$year, nrow)
kbl <- do.call(rbind, list(kbl))
kbl <- kable(x = kbl, format = "simple", format.args = list(big.mark = ","))
kbl
| 14,836 |
6,704 |
10,303 |
14,142 |
15,098 |
8,420 |
8,610 |
8,290 |
8,077 |
7,997 |
8,178 |
# MISSING eq5d % by year
kbl <- by(hse_combined$eq5d,hse_combined$year, function(x){
round( sum(is.na(x))/length(x) , 4) * 100
} )
kbl <- do.call(cbind, list(kbl))
kable(x = kbl, format = "simple",col.names = c("% missing"))
| 2003 |
7.30 |
| 2004 |
8.80 |
| 2005 |
10.60 |
| 2006 |
8.60 |
| 2008 |
6.52 |
| 2010 |
12.92 |
| 2011 |
12.69 |
| 2012 |
12.01 |
| 2014 |
12.28 |
| 2017 |
19.48 |
| 2018 |
20.76 |
# NON-MISSING: rows with complete data
round(sum(complete.cases(hse_combined))/nrow(hse_combined),4)*100
## [1] 34.66
# MISSING data %; by variable
kbl <- apply(hse_combined,2,function(x){
round( sum(is.na(x))/nrow(hse_combined) , 4) * 100
} )
kbl
## year age age.bins imd sex wt
## 26.03 36.84 26.03 26.03 26.03 26.03
## eq5d mo sc ua pd ad
## 34.31 32.14 32.38 32.30 32.16 32.42
## hh_id dis_infect dis_cancer dis_endo dis_blood dis_mental
## 36.84 26.06 26.06 26.06 26.06 26.06
## dis_nerv dis_eye dis_ear dis_circ dis_resp dis_digest
## 26.06 26.06 26.06 26.06 26.06 26.06
## dis_genito dis_skin dis_musculo limitill cigst1 bmival
## 26.06 26.06 26.06 26.07 26.55 37.84
## topqual3 econact eqv5 acutill genhelf2 ghq12scr
## 26.33 37.05 43.99 26.08 26.06 42.95
## age5.bin age5 age_band
## 26.03 26.03 26.03
## Exclude cases without eq5d for now
# EXCLUDE: EQ5D missing
hse_combined <- hse_combined[!is.na(hse_combined$eq5d),]
# Data rows: overall
nrow(hse_combined)
## [1] 98264
## Descriptives: age5
# distribution: age5 by IMD
ggplot(hse_combined) +
geom_histogram(aes(age5), col = "lightgray",fill = "cadetblue") +
facet_wrap(~imd) +
theme_minimal()
## `stat_bin()` using `bins = 30`. Pick better value with `binwidth`.

# distribution: age5 by IMD for age5 >= 85
ggplot(hse_combined[hse_combined$age5>75,]) +
geom_histogram(aes(age5), col = "lightgray",fill = "cadetblue",binwidth = 1) +
facet_wrap(~imd) +
theme_minimal()

## Descriptives: by variable
print(dfSummary(
hse_combined,
plain.ascii = FALSE,
style = "grid",
graph.magnif = 0.75,
valid.col = FALSE,
tmp.img.dir = "/tmp"
),
method = 'render')
TABLES 1+2 and FIGURES 1 + 2: Mean EQ-5D scores
# --------------------------------------------------------
## Descriptive stats: regression model
# > `eq5d = age5 x imd + year`
# age5, imd, and year are treated as factors
imd_lvls <- levels(hse_combined$imd)
uniq_age5 <- unique(hse_combined$age5)
uniq_age5 <- uniq_age5[order(uniq_age5)]
uniq_age5_labels <- c("16-17?","18-20?",paste0(uniq_age5[-c(1,2,17)],"-",uniq_age5[-c(1,2,17)]+4),"90+")
# descriptive regression model - FEMALES
descr_lm_f <- lm(eq5d ~ as.factor(age5) * imd + as.factor(year),hse_combined[hse_combined$sex =="Female",], weights = wt)
# descriptive regression model - ALES
descr_lm_m <- lm(eq5d ~ as.factor(age5) * imd + as.factor(year),hse_combined[hse_combined$sex =="Male",], weights = wt)
# BUT WHICH YEAR SHOULD WE USE TO PREDICT MEANS?
# SIDENOTE: Year doesnt really matter
# 2017 and 2018 are signficantly lower - maybe because of 5L?
year_coef_indices <- grepl("year",names(descr_lm_f$coefficients))
cbind(
descr_lm_f$coefficients[year_coef_indices],
confint(descr_lm_f)[year_coef_indices,]
)
## 2.5 % 97.5 %
## as.factor(year)2004 -4.222163e-03 -0.013738074 0.005293748
## as.factor(year)2005 -2.343558e-03 -0.011476732 0.006789615
## as.factor(year)2006 1.451335e-03 -0.006126911 0.009029581
## as.factor(year)2008 -6.718379e-03 -0.014152244 0.000715486
## as.factor(year)2010 -6.864760e-03 -0.015823548 0.002094028
## as.factor(year)2011 -3.283845e-02 -0.041726970 -0.023949928
## as.factor(year)2012 -4.366429e-03 -0.013335152 0.004602295
## as.factor(year)2014 3.173826e-05 -0.008998183 0.009061660
## as.factor(year)2017 -1.038619e-01 -0.113208009 -0.094515736
## as.factor(year)2018 -1.013141e-01 -0.110667377 -0.091960781
# REGRESSION MODEL WITHOUT YEAR AS PREDICTOR
# descriptive regression model - FEMALES
descr_lm_f <- lm(eq5d ~ as.factor(age5) * imd ,hse_combined[hse_combined$sex =="Female",], weights = wt)
# descriptive regression model - MALES
descr_lm_m <- lm(eq5d ~ as.factor(age5) * imd ,hse_combined[hse_combined$sex =="Male",], weights = wt)
# predict means by strata
# QUESTION: WHICH YEAR SHOULD WE USE TO PREDICT MEANS?
# OR BETTER USE EMPIRICAL AGGREGATE MEANS?
pred_df_f <- pred_df_m <- data.frame(age5 = rep(uniq_age5,5),imd = rep(imd_lvls,each = length(uniq_age5)))
pred_df_f_ratio <- pred_df_m_ratio <- data.frame(age5 = rep(uniq_age5,2),imd = rep(imd_lvls[c(1,5)],each = length(uniq_age5)))
# FIGURE 1
eq5d_pred_m <- predict(descr_lm_m, newdata = pred_df_m,interval ="predict")
## Warning in predict.lm(descr_lm_m, newdata = pred_df_m, interval = "predict"): Assuming constant prediction variance even though model fit is weighted
eq5d_pred_m_plot <- cbind(pred_df_m, eq5d_pred_m)
figure1 <- ggplot(eq5d_pred_m_plot) +
# geom_ribbon(aes(x=age5, ymin = lwr, ymax = upr, fill = imd), alpha = 0.3) +
geom_point(aes(x=age5, y = fit, col = imd)) +
geom_line(aes(x=age5, y = fit, col = imd)) +
ylim(c(0,1)) +
ylab("Mean EQ-5D index") +
xlab("Age group") +
scale_x_continuous(name = "Age group", breaks = uniq_age5, labels = uniq_age5_labels) +
ggtitle("Pooled mean EQ-5D Score by IMD quintile and age - MALE") +
theme_minimal() +
theme(
axis.text.x = element_text(angle = 30),
legend.position = "top")
figure1

ggsave(plot = figure1, filename = "./outputs/figure1.jpg",height = 5, width = 7)
# TABLE 1
eq5d_pred_m <- formatC(eq5d_pred_m,digits = 2,format = "f")
eq5d_pred_m <- paste0(eq5d_pred_m[,1]," (",eq5d_pred_m[,2],"; ",eq5d_pred_m[,3],")")
pred_df_m$eq5d <- eq5d_pred_m
# the inequality ratio ci are bootstrapped
ineq_m <- ineq_boot1(hse_combined, "Male",boot_iter = 1000)
## Loading required package: data.table
##
## Attaching package: 'data.table'
## The following object is masked from 'package:ggplot2':
##
## :=
## The following objects are masked from 'package:dplyr':
##
## between, first, last
## The following objects are masked from 'package:reshape2':
##
## dcast, melt
ineq_m_formated <- formatC(as.matrix(ineq_m$plot_df),digits = 2,format = "f")
ineq_m_formated <- paste0(ineq_m_formated[,2]," (",ineq_m_formated[,3],"; ",ineq_m_formated[,4],")")
pred_df_m <- reshape(pred_df_m,direction = "wide",timevar = "imd" ,idvar = "age5")
pred_df_m <- cbind(pred_df_m, "most/least ratio" = ineq_m_formated)
pred_df_m
write.csv(pred_df_m, "./outputs/table1.csv", row.names = F)
# FIGURE 2
eq5d_pred_f <- predict(descr_lm_f, newdata = pred_df_f,interval ="predict")
## Warning in predict.lm(descr_lm_f, newdata = pred_df_f, interval = "predict"): Assuming constant prediction variance even though model fit is weighted
eq5d_pred_f_plot <- cbind(pred_df_f, eq5d_pred_f)
figure2 <- ggplot(eq5d_pred_f_plot) +
# geom_ribbon(aes(x=age5, ymin = lwr, ymax = upr, fill = imd), alpha = 0.3) +
geom_point(aes(x=age5, y = fit, col = imd)) +
geom_line(aes(x=age5, y = fit, col = imd)) +
ylim(c(0,1)) +
ylab("Mean EQ-5D index") +
xlab("Age group") +
scale_x_continuous(name = "Age group", breaks = uniq_age5, labels = uniq_age5_labels) +
ggtitle("Pooled mean EQ-5D Score by IMD quintile and age - FEMALE") +
theme_minimal() +
theme(
axis.text.x = element_text(angle = 30),
legend.position = "top")
figure2

ggsave(plot = figure2, filename = "./outputs/figure2.jpg",height = 5, width = 7)
# TABLE 2
eq5d_pred_f <- formatC(eq5d_pred_f,digits = 2,format = "f")
eq5d_pred_f <- paste0(eq5d_pred_f[,1]," (",eq5d_pred_f[,2],"; ",eq5d_pred_f[,3],")")
pred_df_f$eq5d <- eq5d_pred_f
# bootstrap ineq ratio
ineq_f <- ineq_boot1(hse_combined, "Female",boot_iter = 1000)
ineq_f_formated <- formatC(as.matrix(ineq_f$plot_df),digits = 2,format = "f")
ineq_f_formated <- paste0(ineq_f_formated[,2]," (",ineq_f_formated[,3],"; ",ineq_f_formated[,4],")")
pred_df_f <- reshape(pred_df_f,direction = "wide",timevar = "imd" ,idvar = "age5")
pred_df_f <- cbind(pred_df_f, "most/least ratio" = ineq_f_formated)
pred_df_f
write.csv(pred_df_f, "./outputs/table2.csv", row.names = F)
SIDENOTE: Any difference in variance between male and female?
# Yes, females have about 7% higher SD
eq5d_sd_sex_tot <- aggregate(eq5d ~ sex, hse_combined, sd)
(eq5d_sd_sex_tot$eq5d[1] / eq5d_sd_sex_tot$eq5d[2] - 1) * 100
## [1] 7.514204
eq5d_sd_sex <- aggregate(eq5d ~ sex + age5, hse_combined, sd)
eq5d_sd_sex <- reshape(eq5d_sd_sex,direction = "wide",timevar = "sex" ,idvar = "age5")
eq5d_sd_sex$female_sd_proz <- ( (eq5d_sd_sex$eq5d.Female / eq5d_sd_sex$eq5d.Male) - 1) * 100
eq5d_sd_sex
FIGURES 7 + 8: Concentration index value by age - PRE / POST 2017 bc 5L
load("ciResults_markdown.RData")
# Dimension plots for unweighted analysis
ggplot(filter(allCIResults,Period=="Pre17"), aes(x=Age,y=CI,group=Sex,col=Sex,fill=Sex)) +
geom_line() + geom_hline(yintercept=0, colour="black") +
ylab("Concentration Index") + theme_minimal() +
xlab("Age") + facet_wrap(~Dimension) +
geom_ribbon(aes(ymin=CI_lci, ymax=CI_uci),alpha=0.2,colour=NA) +
ggtitle("3L 2003-2014")

ggplot(filter(allCIResults,Period=="Post17"), aes(x=Age,y=CI,group=Sex,col=Sex,fill=Sex)) +
geom_line() + geom_hline(yintercept=0, colour="black") +
ylab("Concentration Index") + theme_minimal() +
xlab("Age") + facet_wrap(~Dimension) +
geom_ribbon(aes(ymin=CI_lci, ymax=CI_uci),alpha=0.2,colour=NA) +
ggtitle("5L 2017+2018")

FIGURES 9 + 10: Proportion reporting each level of response for each EQ-5D dimension
# FIGURE 9
# ONLY FOR 3L DIMENSIONS 2003-2014 !
# EXCLUDING 2017 + 2018
# source("./R/utilities.R")
dim_agg1 <- dimGetter(hse_combined[hse_combined$year <= 2014,], 1)
dim_agg2 <- dimGetter(hse_combined[hse_combined$year <= 2014,], 2)
dim_agg3 <- dimGetter(hse_combined[hse_combined$year <= 2014,], 3)
dim_aggs <- merge(dim_agg1,dim_agg2, all.x=T, all.y = T)
dim_aggs <- merge(dim_aggs,dim_agg3, all.x=T, all.y = T)
dim_aggs_long = melt(dim_aggs,id.vars = c("imd","sex","age5","level"))
dim_aggs_long_rel <- dim_aggs_long[grepl("rel",dim_aggs_long$variable),]
dim_aggs_long_rel$variable = as.character(dim_aggs_long_rel$variable)
keep_var <- dim_aggs_long_rel$level == as.numeric( substr(dim_aggs_long_rel$variable,nchar(dim_aggs_long_rel$variable),nchar(dim_aggs_long_rel$variable)))
dim_aggs_long_rel <- dim_aggs_long_rel[keep_var,]
# dim_aggs_long_rel$value[is.na(dim_aggs_long_rel$value)] <- 0
dim_aggs_long_rel$dimension = substr(dim_aggs_long_rel$variable,1,2)
dim_aggs_long_rel$dimension = factor(dim_aggs_long_rel$dimension,
levels=c("mo","sc","ua","pd","ad")
)
dim_aggs_long_rel$dimension <- recode(
dim_aggs_long_rel$dimension,
mo = "Mobility",
sc = "Self-care",
ua = "Usual activities",
pd = "Pain/Discomfort",
ad = "Anxiety/Depression"
)
col_labs <- c("No problems","Some problems","Extreme problems")
figure9 <- ggplot(dim_aggs_long_rel[dim_aggs_long_rel$sex=="Male",],
aes(x=as.factor(age5), y=value,fill=as.factor(level),
col=as.factor(level))) +
geom_col(position = 'stack',alpha=0.6) +
facet_grid(dimension ~ imd) +
scale_color_manual(values = c("lightgreen","orange","red"),
labels = c(col_labs),name="Level") +
scale_fill_manual(values = c("lightgreen","orange","red"),
labels = c(col_labs),name="Level") +
xlab("Age group") +
ylab("Proportion") +
theme_minimal()+
theme(legend.position = "bottom") +
ggtitle("Proportion reporting each level of response for each EQ-5D dimension over age - Males") +
theme_minimal() +
theme(
axis.text.x = element_text(angle = 45),
legend.position = "top")
figure9
## Warning: Removed 113 rows containing missing values (position_stack).

ggsave(plot = figure9, filename = "./outputs/figure9.jpg",height = 8, width = 9)
## Warning: Removed 113 rows containing missing values (position_stack).
figure10 <- ggplot(dim_aggs_long_rel[dim_aggs_long_rel$sex=="Female",],
aes(x=as.factor(age5), y=value,fill=as.factor(level),
col=as.factor(level))) +
geom_col(position = 'stack',alpha=0.6) +
facet_grid(dimension ~ imd) +
scale_color_manual(values = c("lightgreen","orange","red"),
labels = c(col_labs),name="Level") +
scale_fill_manual(values = c("lightgreen","orange","red"),
labels = c(col_labs),name="Level") +
xlab("Age group") +
ylab("Proportion") +
theme_minimal()+
theme(legend.position = "bottom") +
ggtitle("Proportion reporting each level of response for each EQ-5D dimension over age - Females") +
theme_minimal() +
theme(
axis.text.x = element_text(angle = 45),
legend.position = "top")
figure10
## Warning: Removed 96 rows containing missing values (position_stack).

ggsave(plot = figure10, filename = "./outputs/figure10.jpg",height = 8, width = 9)
## Warning: Removed 96 rows containing missing values (position_stack).
FIGURES 11 - 16: IMD1/IMD5 ratio of % reporting problems for each EQ-5D dimension
# ------------------------------------------------------------------------
#### Line graph showing ratio of IMD1/IMD5 for each EQ-5D dimension over age
# FIGURE 11
figure11 <- dimProbPlotter(dim_aggs_long_rel,1,label = "No Problems","Male")
figure11

ggsave(plot = figure11, filename = "./outputs/figure11.jpg",height = 8, width = 9)
# FIGURE 12
figure12 <- dimProbPlotter(dim_aggs_long_rel,1,label = "No Problems","Female")
figure12

ggsave(plot = figure12, filename = "./outputs/figure12.jpg",height = 8, width = 9)
# FIGURE 13
figure13 <- dimProbPlotter(dim_aggs_long_rel,2,label = "Some Problems","Male")
figure13

ggsave(plot = figure13, filename = "./outputs/figure13.jpg",height = 8, width = 9)
# FIGURE 14
figure14 <- dimProbPlotter(dim_aggs_long_rel,2,label = "Some Problems","Female")
figure14

ggsave(plot = figure14, filename = "./outputs/figure14.jpg",height = 8, width = 9)
# FIGURE 15
figure15 <- dimProbPlotter(dim_aggs_long_rel,3,label = "Extreme Problems","Male")
figure15
## Warning: Removed 6 row(s) containing missing values (geom_path).

ggsave(plot = figure15, filename = "./outputs/figure15.jpg",height = 8, width = 9)
## Warning: Removed 6 row(s) containing missing values (geom_path).
# FIGURE 16
figure16 <- dimProbPlotter(dim_aggs_long_rel,3,label = "Extreme Problems","Female")
figure16
## Warning: Removed 6 row(s) containing missing values (geom_path).

ggsave(plot = figure16, filename = "./outputs/figure16.jpg",height = 8, width = 9)
## Warning: Removed 6 row(s) containing missing values (geom_path).
TABLES 3-12
uniq_sex <- unique(dim_aggs_long_rel$sex)
uniq_dims <- unique(dim_aggs_long_rel$dimension)
uniq_imd <- as.character(unique(dim_aggs_long_rel$imd))
dimPropTbler <- function(dim_aggs_long_rel,dim = "Mobility",sex = "Male"){
dim_aggs_long_rel_mo <- dim_aggs_long_rel[dim_aggs_long_rel$dimension == dim & dim_aggs_long_rel$sex == sex,]
dim_aggs_long_rel_mo <- dim_aggs_long_rel_mo[,c("imd","age5","level","value")]
dim_aggs_long_rel_mo$value[is.na(dim_aggs_long_rel_mo$value)] <- 0
dim_aggs_long_rel_mo$value <- round(dim_aggs_long_rel_mo$value,2)*100
dim_aggs_long_rel_mo$value <- paste0(dim_aggs_long_rel_mo$value," %")
dim_aggs_long_rel_mo_tbl <- reshape(dim_aggs_long_rel_mo,direction = "wide", idvar = c("level", "age5"), timevar = "imd")
dim_aggs_long_rel_mo_tbl <- dim_aggs_long_rel_mo_tbl[order(dim_aggs_long_rel_mo_tbl$age5),]
reps <- sum(dim_aggs_long_rel_mo_tbl$age5 == dim_aggs_long_rel_mo_tbl$age5[1])
dim_aggs_long_rel_mo_tbl$age5 <- rep(uniq_age5_labels,each = reps)
dim_aggs_long_rel_mo_tbl$age5 <- paste0("age ",dim_aggs_long_rel_mo_tbl$age5)
dim_aggs_long_rel_mo_tbl$level <- paste0("lvl ",dim_aggs_long_rel_mo_tbl$level)
rownames(dim_aggs_long_rel_mo_tbl) <- NULL
return(dim_aggs_long_rel_mo_tbl)
}
# create % problems by dimension, age, imd and sex tables
tbl_index = 3
tbl_list <- list()
for(s in uniq_sex){
for(d in uniq_dims){
tbl_t <- dimPropTbler(dim_aggs_long_rel, dim = d,sex = s)
write.csv(tbl_t,file = paste0("./outputs/table",tbl_index))
tbl_list[tbl_index-2] <- kable(
tbl_t[,-c(1)],
col.names = c("level",uniq_imd),
caption = paste0(d," - ",s),
booktabs = T) %>%
kableExtra::group_rows(index = table(tbl_t$age5)) %>%
kable_styling( full_width = F, position = "left")
tbl_index <- tbl_index + 1
}
}
# # print results
for(l in tbl_list){
print(shiny::HTML(l))
cat("\n")
cat("<br>")
cat("<hr>")
cat("<br>")
}
Mobility - Female
|
level
|
Least deprived
|
Less deprived
|
Median deprived
|
More deprieved
|
Most deprived
|
|
age 16-17?
|
|
lvl 1
|
96 %
|
96 %
|
98 %
|
98 %
|
99 %
|
|
lvl 2
|
4 %
|
4 %
|
2 %
|
2 %
|
1 %
|
|
lvl 3
|
1 %
|
0 %
|
0 %
|
0 %
|
0 %
|
|
age 18-20?
|
|
lvl 1
|
97 %
|
97 %
|
97 %
|
98 %
|
91 %
|
|
lvl 2
|
3 %
|
3 %
|
3 %
|
2 %
|
9 %
|
|
lvl 3
|
0 %
|
0 %
|
0 %
|
0 %
|
0 %
|
|
age 20-24
|
|
lvl 1
|
97 %
|
97 %
|
95 %
|
96 %
|
94 %
|
|
lvl 2
|
3 %
|
3 %
|
5 %
|
4 %
|
6 %
|
|
lvl 3
|
0 %
|
0 %
|
0 %
|
0 %
|
0 %
|
|
age 25-29
|
|
lvl 1
|
96 %
|
96 %
|
97 %
|
94 %
|
94 %
|
|
lvl 2
|
4 %
|
4 %
|
3 %
|
6 %
|
5 %
|
|
lvl 3
|
0 %
|
0 %
|
0 %
|
0 %
|
0 %
|
|
age 30-34
|
|
lvl 1
|
97 %
|
96 %
|
95 %
|
93 %
|
91 %
|
|
lvl 2
|
3 %
|
4 %
|
5 %
|
7 %
|
9 %
|
|
lvl 3
|
0 %
|
0 %
|
0 %
|
0 %
|
0 %
|
|
age 35-39
|
|
lvl 1
|
96 %
|
93 %
|
93 %
|
91 %
|
87 %
|
|
lvl 2
|
4 %
|
7 %
|
6 %
|
9 %
|
13 %
|
|
lvl 3
|
0 %
|
0 %
|
0 %
|
0 %
|
0 %
|
|
age 40-44
|
|
lvl 1
|
95 %
|
93 %
|
91 %
|
89 %
|
82 %
|
|
lvl 2
|
5 %
|
7 %
|
9 %
|
11 %
|
18 %
|
|
lvl 3
|
0 %
|
0 %
|
0 %
|
0 %
|
1 %
|
|
age 45-49
|
|
lvl 1
|
92 %
|
91 %
|
88 %
|
84 %
|
75 %
|
|
lvl 2
|
8 %
|
9 %
|
12 %
|
15 %
|
25 %
|
|
lvl 3
|
0 %
|
0 %
|
0 %
|
0 %
|
0 %
|
|
age 50-54
|
|
lvl 1
|
89 %
|
86 %
|
81 %
|
76 %
|
67 %
|
|
lvl 2
|
11 %
|
14 %
|
19 %
|
24 %
|
32 %
|
|
lvl 3
|
0 %
|
0 %
|
0 %
|
0 %
|
1 %
|
|
age 55-59
|
|
lvl 1
|
86 %
|
81 %
|
77 %
|
73 %
|
63 %
|
|
lvl 2
|
14 %
|
19 %
|
23 %
|
27 %
|
37 %
|
|
lvl 3
|
0 %
|
0 %
|
0 %
|
0 %
|
0 %
|
|
age 60-64
|
|
lvl 1
|
83 %
|
79 %
|
75 %
|
70 %
|
60 %
|
|
lvl 2
|
17 %
|
21 %
|
25 %
|
30 %
|
40 %
|
|
lvl 3
|
0 %
|
0 %
|
0 %
|
0 %
|
0 %
|
|
age 65-69
|
|
lvl 1
|
78 %
|
76 %
|
72 %
|
63 %
|
58 %
|
|
lvl 2
|
22 %
|
24 %
|
28 %
|
36 %
|
42 %
|
|
lvl 3
|
0 %
|
0 %
|
0 %
|
0 %
|
0 %
|
|
age 70-74
|
|
lvl 1
|
70 %
|
65 %
|
61 %
|
56 %
|
55 %
|
|
lvl 2
|
30 %
|
35 %
|
39 %
|
44 %
|
45 %
|
|
lvl 3
|
0 %
|
0 %
|
0 %
|
0 %
|
0 %
|
|
age 75-79
|
|
lvl 1
|
65 %
|
53 %
|
56 %
|
51 %
|
43 %
|
|
lvl 2
|
35 %
|
47 %
|
44 %
|
48 %
|
57 %
|
|
lvl 3
|
0 %
|
0 %
|
0 %
|
0 %
|
0 %
|
|
age 80-84
|
|
lvl 1
|
50 %
|
50 %
|
43 %
|
40 %
|
30 %
|
|
lvl 2
|
50 %
|
50 %
|
56 %
|
60 %
|
69 %
|
|
lvl 3
|
0 %
|
0 %
|
1 %
|
1 %
|
1 %
|
|
age 85-89
|
|
lvl 1
|
37 %
|
36 %
|
30 %
|
24 %
|
27 %
|
|
lvl 2
|
63 %
|
64 %
|
70 %
|
76 %
|
73 %
|
|
lvl 3
|
0 %
|
0 %
|
0 %
|
0 %
|
0 %
|
|
age 90+
|
|
lvl 1
|
31 %
|
21 %
|
24 %
|
31 %
|
11 %
|
|
lvl 2
|
69 %
|
77 %
|
74 %
|
67 %
|
89 %
|
|
lvl 3
|
0 %
|
1 %
|
1 %
|
2 %
|
0 %
|
Self-care - Female
|
level
|
Least deprived
|
Less deprived
|
Median deprived
|
More deprieved
|
Most deprived
|
|
age 16-17?
|
|
lvl 1
|
98 %
|
100 %
|
99 %
|
99 %
|
100 %
|
|
lvl 2
|
1 %
|
0 %
|
1 %
|
1 %
|
0 %
|
|
lvl 3
|
1 %
|
0 %
|
0 %
|
0 %
|
0 %
|
|
age 18-20?
|
|
lvl 1
|
100 %
|
99 %
|
100 %
|
98 %
|
100 %
|
|
lvl 2
|
0 %
|
1 %
|
0 %
|
2 %
|
0 %
|
|
lvl 3
|
0 %
|
0 %
|
0 %
|
0 %
|
0 %
|
|
age 20-24
|
|
lvl 1
|
100 %
|
100 %
|
99 %
|
99 %
|
98 %
|
|
lvl 2
|
0 %
|
0 %
|
1 %
|
1 %
|
1 %
|
|
lvl 3
|
0 %
|
0 %
|
0 %
|
0 %
|
0 %
|
|
age 25-29
|
|
lvl 1
|
99 %
|
99 %
|
99 %
|
99 %
|
98 %
|
|
lvl 2
|
1 %
|
1 %
|
1 %
|
1 %
|
2 %
|
|
lvl 3
|
0 %
|
0 %
|
0 %
|
0 %
|
0 %
|
|
age 30-34
|
|
lvl 1
|
100 %
|
98 %
|
99 %
|
98 %
|
97 %
|
|
lvl 2
|
0 %
|
1 %
|
1 %
|
2 %
|
2 %
|
|
lvl 3
|
0 %
|
0 %
|
0 %
|
0 %
|
0 %
|
|
age 35-39
|
|
lvl 1
|
99 %
|
98 %
|
98 %
|
97 %
|
96 %
|
|
lvl 2
|
1 %
|
1 %
|
1 %
|
3 %
|
4 %
|
|
lvl 3
|
0 %
|
0 %
|
0 %
|
0 %
|
0 %
|
|
age 40-44
|
|
lvl 1
|
99 %
|
98 %
|
98 %
|
96 %
|
93 %
|
|
lvl 2
|
1 %
|
2 %
|
2 %
|
4 %
|
7 %
|
|
lvl 3
|
0 %
|
0 %
|
0 %
|
0 %
|
0 %
|
|
age 45-49
|
|
lvl 1
|
99 %
|
98 %
|
96 %
|
95 %
|
91 %
|
|
lvl 2
|
1 %
|
2 %
|
4 %
|
4 %
|
8 %
|
|
lvl 3
|
0 %
|
0 %
|
0 %
|
0 %
|
1 %
|
|
age 50-54
|
|
lvl 1
|
98 %
|
96 %
|
95 %
|
92 %
|
86 %
|
|
lvl 2
|
2 %
|
4 %
|
5 %
|
8 %
|
13 %
|
|
lvl 3
|
0 %
|
0 %
|
1 %
|
0 %
|
1 %
|
|
age 55-59
|
|
lvl 1
|
96 %
|
96 %
|
93 %
|
89 %
|
85 %
|
|
lvl 2
|
4 %
|
4 %
|
7 %
|
10 %
|
14 %
|
|
lvl 3
|
0 %
|
0 %
|
0 %
|
0 %
|
1 %
|
|
age 60-64
|
|
lvl 1
|
97 %
|
95 %
|
92 %
|
92 %
|
85 %
|
|
lvl 2
|
3 %
|
4 %
|
7 %
|
8 %
|
14 %
|
|
lvl 3
|
0 %
|
0 %
|
0 %
|
0 %
|
1 %
|
|
age 65-69
|
|
lvl 1
|
97 %
|
93 %
|
93 %
|
90 %
|
86 %
|
|
lvl 2
|
3 %
|
6 %
|
7 %
|
10 %
|
13 %
|
|
lvl 3
|
0 %
|
0 %
|
0 %
|
1 %
|
1 %
|
|
age 70-74
|
|
lvl 1
|
94 %
|
91 %
|
93 %
|
87 %
|
86 %
|
|
lvl 2
|
6 %
|
9 %
|
7 %
|
12 %
|
13 %
|
|
lvl 3
|
0 %
|
0 %
|
0 %
|
1 %
|
1 %
|
|
age 75-79
|
|
lvl 1
|
93 %
|
88 %
|
89 %
|
89 %
|
78 %
|
|
lvl 2
|
6 %
|
12 %
|
10 %
|
10 %
|
22 %
|
|
lvl 3
|
0 %
|
0 %
|
1 %
|
1 %
|
0 %
|
|
age 80-84
|
|
lvl 1
|
86 %
|
88 %
|
81 %
|
85 %
|
78 %
|
|
lvl 2
|
13 %
|
11 %
|
18 %
|
13 %
|
21 %
|
|
lvl 3
|
1 %
|
1 %
|
2 %
|
1 %
|
1 %
|
|
age 85-89
|
|
lvl 1
|
82 %
|
78 %
|
79 %
|
75 %
|
66 %
|
|
lvl 2
|
17 %
|
22 %
|
19 %
|
23 %
|
32 %
|
|
lvl 3
|
2 %
|
1 %
|
2 %
|
2 %
|
3 %
|
|
age 90+
|
|
lvl 1
|
70 %
|
68 %
|
66 %
|
68 %
|
57 %
|
|
lvl 2
|
28 %
|
28 %
|
28 %
|
27 %
|
41 %
|
|
lvl 3
|
1 %
|
4 %
|
6 %
|
5 %
|
3 %
|
Usual activities - Female
|
level
|
Least deprived
|
Less deprived
|
Median deprived
|
More deprieved
|
Most deprived
|
|
age 16-17?
|
|
lvl 1
|
95 %
|
97 %
|
97 %
|
97 %
|
97 %
|
|
lvl 2
|
4 %
|
3 %
|
3 %
|
3 %
|
3 %
|
|
lvl 3
|
1 %
|
0 %
|
0 %
|
1 %
|
0 %
|
|
age 18-20?
|
|
lvl 1
|
97 %
|
94 %
|
97 %
|
96 %
|
90 %
|
|
lvl 2
|
3 %
|
6 %
|
3 %
|
4 %
|
9 %
|
|
lvl 3
|
0 %
|
0 %
|
0 %
|
0 %
|
0 %
|
|
age 20-24
|
|
lvl 1
|
95 %
|
95 %
|
91 %
|
95 %
|
92 %
|
|
lvl 2
|
5 %
|
5 %
|
8 %
|
5 %
|
7 %
|
|
lvl 3
|
0 %
|
0 %
|
0 %
|
0 %
|
1 %
|
|
age 25-29
|
|
lvl 1
|
94 %
|
93 %
|
93 %
|
93 %
|
90 %
|
|
lvl 2
|
5 %
|
7 %
|
6 %
|
7 %
|
10 %
|
|
lvl 3
|
1 %
|
0 %
|
0 %
|
0 %
|
0 %
|
|
age 30-34
|
|
lvl 1
|
93 %
|
92 %
|
93 %
|
91 %
|
90 %
|
|
lvl 2
|
7 %
|
7 %
|
6 %
|
8 %
|
9 %
|
|
lvl 3
|
0 %
|
0 %
|
0 %
|
1 %
|
1 %
|
|
age 35-39
|
|
lvl 1
|
93 %
|
92 %
|
91 %
|
89 %
|
86 %
|
|
lvl 2
|
7 %
|
8 %
|
8 %
|
11 %
|
13 %
|
|
lvl 3
|
0 %
|
0 %
|
1 %
|
1 %
|
1 %
|
|
age 40-44
|
|
lvl 1
|
92 %
|
92 %
|
89 %
|
86 %
|
82 %
|
|
lvl 2
|
7 %
|
7 %
|
11 %
|
13 %
|
17 %
|
|
lvl 3
|
1 %
|
1 %
|
0 %
|
1 %
|
2 %
|
|
age 45-49
|
|
lvl 1
|
91 %
|
89 %
|
88 %
|
83 %
|
76 %
|
|
lvl 2
|
9 %
|
10 %
|
11 %
|
16 %
|
22 %
|
|
lvl 3
|
0 %
|
1 %
|
2 %
|
1 %
|
2 %
|
|
age 50-54
|
|
lvl 1
|
90 %
|
85 %
|
81 %
|
76 %
|
68 %
|
|
lvl 2
|
10 %
|
13 %
|
16 %
|
21 %
|
28 %
|
|
lvl 3
|
0 %
|
2 %
|
3 %
|
3 %
|
4 %
|
|
age 55-59
|
|
lvl 1
|
86 %
|
82 %
|
78 %
|
75 %
|
65 %
|
|
lvl 2
|
13 %
|
17 %
|
20 %
|
22 %
|
31 %
|
|
lvl 3
|
1 %
|
1 %
|
2 %
|
3 %
|
4 %
|
|
age 60-64
|
|
lvl 1
|
85 %
|
81 %
|
77 %
|
74 %
|
66 %
|
|
lvl 2
|
14 %
|
17 %
|
20 %
|
24 %
|
31 %
|
|
lvl 3
|
1 %
|
1 %
|
2 %
|
2 %
|
4 %
|
|
age 65-69
|
|
lvl 1
|
84 %
|
80 %
|
80 %
|
71 %
|
66 %
|
|
lvl 2
|
15 %
|
18 %
|
19 %
|
27 %
|
29 %
|
|
lvl 3
|
1 %
|
2 %
|
2 %
|
2 %
|
5 %
|
|
age 70-74
|
|
lvl 1
|
76 %
|
73 %
|
71 %
|
65 %
|
60 %
|
|
lvl 2
|
22 %
|
26 %
|
27 %
|
32 %
|
36 %
|
|
lvl 3
|
1 %
|
2 %
|
2 %
|
3 %
|
5 %
|
|
age 75-79
|
|
lvl 1
|
74 %
|
67 %
|
64 %
|
63 %
|
51 %
|
|
lvl 2
|
23 %
|
31 %
|
31 %
|
31 %
|
42 %
|
|
lvl 3
|
2 %
|
3 %
|
4 %
|
6 %
|
7 %
|
|
age 80-84
|
|
lvl 1
|
57 %
|
65 %
|
56 %
|
49 %
|
47 %
|
|
lvl 2
|
37 %
|
31 %
|
38 %
|
43 %
|
43 %
|
|
lvl 3
|
6 %
|
4 %
|
6 %
|
7 %
|
9 %
|
|
age 85-89
|
|
lvl 1
|
46 %
|
44 %
|
43 %
|
35 %
|
36 %
|
|
lvl 2
|
47 %
|
48 %
|
48 %
|
56 %
|
50 %
|
|
lvl 3
|
7 %
|
8 %
|
9 %
|
9 %
|
14 %
|
|
age 90+
|
|
lvl 1
|
42 %
|
39 %
|
36 %
|
40 %
|
22 %
|
|
lvl 2
|
47 %
|
52 %
|
46 %
|
47 %
|
56 %
|
|
lvl 3
|
11 %
|
9 %
|
18 %
|
13 %
|
22 %
|
Pain/Discomfort - Female
|
level
|
Least deprived
|
Less deprived
|
Median deprived
|
More deprieved
|
Most deprived
|
|
age 16-17?
|
|
lvl 1
|
87 %
|
92 %
|
83 %
|
86 %
|
84 %
|
|
lvl 2
|
13 %
|
8 %
|
17 %
|
14 %
|
15 %
|
|
lvl 3
|
1 %
|
0 %
|
0 %
|
0 %
|
1 %
|
|
age 18-20?
|
|
lvl 1
|
83 %
|
84 %
|
85 %
|
87 %
|
79 %
|
|
lvl 2
|
17 %
|
16 %
|
14 %
|
12 %
|
19 %
|
|
lvl 3
|
0 %
|
0 %
|
0 %
|
0 %
|
2 %
|
|
age 20-24
|
|
lvl 1
|
84 %
|
88 %
|
84 %
|
85 %
|
82 %
|
|
lvl 2
|
15 %
|
12 %
|
14 %
|
14 %
|
17 %
|
|
lvl 3
|
0 %
|
0 %
|
2 %
|
2 %
|
1 %
|
|
age 25-29
|
|
lvl 1
|
84 %
|
84 %
|
87 %
|
83 %
|
81 %
|
|
lvl 2
|
15 %
|
16 %
|
13 %
|
15 %
|
18 %
|
|
lvl 3
|
1 %
|
1 %
|
1 %
|
2 %
|
1 %
|
|
age 30-34
|
|
lvl 1
|
86 %
|
85 %
|
80 %
|
80 %
|
76 %
|
|
lvl 2
|
13 %
|
14 %
|
19 %
|
18 %
|
21 %
|
|
lvl 3
|
1 %
|
1 %
|
1 %
|
2 %
|
4 %
|
|
age 35-39
|
|
lvl 1
|
84 %
|
78 %
|
78 %
|
74 %
|
69 %
|
|
lvl 2
|
16 %
|
20 %
|
20 %
|
24 %
|
26 %
|
|
lvl 3
|
1 %
|
2 %
|
2 %
|
2 %
|
4 %
|
|
age 40-44
|
|
lvl 1
|
79 %
|
77 %
|
74 %
|
69 %
|
63 %
|
|
lvl 2
|
19 %
|
20 %
|
24 %
|
27 %
|
31 %
|
|
lvl 3
|
1 %
|
2 %
|
2 %
|
3 %
|
6 %
|
|
age 45-49
|
|
lvl 1
|
74 %
|
72 %
|
70 %
|
65 %
|
55 %
|
|
lvl 2
|
25 %
|
26 %
|
27 %
|
30 %
|
38 %
|
|
lvl 3
|
2 %
|
2 %
|
3 %
|
4 %
|
7 %
|
|
age 50-54
|
|
lvl 1
|
68 %
|
65 %
|
56 %
|
53 %
|
47 %
|
|
lvl 2
|
30 %
|
33 %
|
39 %
|
40 %
|
41 %
|
|
lvl 3
|
2 %
|
3 %
|
5 %
|
7 %
|
12 %
|
|
age 55-59
|
|
lvl 1
|
66 %
|
60 %
|
55 %
|
52 %
|
41 %
|
|
lvl 2
|
31 %
|
35 %
|
38 %
|
38 %
|
43 %
|
|
lvl 3
|
3 %
|
4 %
|
6 %
|
9 %
|
16 %
|
|
age 60-64
|
|
lvl 1
|
60 %
|
57 %
|
50 %
|
47 %
|
39 %
|
|
lvl 2
|
37 %
|
39 %
|
43 %
|
44 %
|
48 %
|
|
lvl 3
|
2 %
|
5 %
|
7 %
|
8 %
|
13 %
|
|
age 65-69
|
|
lvl 1
|
58 %
|
54 %
|
49 %
|
45 %
|
38 %
|
|
lvl 2
|
38 %
|
40 %
|
46 %
|
45 %
|
49 %
|
|
lvl 3
|
4 %
|
6 %
|
5 %
|
9 %
|
13 %
|
|
age 70-74
|
|
lvl 1
|
49 %
|
44 %
|
43 %
|
37 %
|
34 %
|
|
lvl 2
|
47 %
|
50 %
|
50 %
|
53 %
|
52 %
|
|
lvl 3
|
4 %
|
6 %
|
7 %
|
10 %
|
14 %
|
|
age 75-79
|
|
lvl 1
|
48 %
|
37 %
|
39 %
|
38 %
|
29 %
|
|
lvl 2
|
48 %
|
54 %
|
54 %
|
54 %
|
57 %
|
|
lvl 3
|
4 %
|
9 %
|
7 %
|
8 %
|
14 %
|
|
age 80-84
|
|
lvl 1
|
37 %
|
34 %
|
36 %
|
36 %
|
30 %
|
|
lvl 2
|
53 %
|
57 %
|
55 %
|
53 %
|
56 %
|
|
lvl 3
|
10 %
|
9 %
|
9 %
|
11 %
|
14 %
|
|
age 85-89
|
|
lvl 1
|
34 %
|
31 %
|
28 %
|
28 %
|
32 %
|
|
lvl 2
|
57 %
|
62 %
|
57 %
|
58 %
|
52 %
|
|
lvl 3
|
8 %
|
7 %
|
15 %
|
14 %
|
17 %
|
|
age 90+
|
|
lvl 1
|
37 %
|
39 %
|
36 %
|
44 %
|
26 %
|
|
lvl 2
|
53 %
|
50 %
|
57 %
|
44 %
|
56 %
|
|
lvl 3
|
10 %
|
11 %
|
8 %
|
12 %
|
18 %
|
Anxiety/Depression - Female
|
level
|
Least deprived
|
Less deprived
|
Median deprived
|
More deprieved
|
Most deprived
|
|
age 16-17?
|
|
lvl 1
|
83 %
|
86 %
|
83 %
|
84 %
|
87 %
|
|
lvl 2
|
16 %
|
13 %
|
17 %
|
16 %
|
12 %
|
|
lvl 3
|
1 %
|
1 %
|
0 %
|
1 %
|
1 %
|
|
age 18-20?
|
|
lvl 1
|
83 %
|
79 %
|
86 %
|
85 %
|
84 %
|
|
lvl 2
|
16 %
|
20 %
|
12 %
|
14 %
|
12 %
|
|
lvl 3
|
1 %
|
1 %
|
3 %
|
1 %
|
4 %
|
|
age 20-24
|
|
lvl 1
|
80 %
|
81 %
|
80 %
|
80 %
|
79 %
|
|
lvl 2
|
18 %
|
18 %
|
19 %
|
19 %
|
19 %
|
|
lvl 3
|
2 %
|
1 %
|
1 %
|
1 %
|
2 %
|
|
age 25-29
|
|
lvl 1
|
85 %
|
82 %
|
81 %
|
80 %
|
79 %
|
|
lvl 2
|
14 %
|
16 %
|
18 %
|
18 %
|
18 %
|
|
lvl 3
|
1 %
|
1 %
|
1 %
|
2 %
|
3 %
|
|
age 30-34
|
|
lvl 1
|
85 %
|
85 %
|
82 %
|
80 %
|
76 %
|
|
lvl 2
|
14 %
|
15 %
|
16 %
|
18 %
|
21 %
|
|
lvl 3
|
1 %
|
0 %
|
2 %
|
2 %
|
3 %
|
|
age 35-39
|
|
lvl 1
|
82 %
|
82 %
|
81 %
|
77 %
|
72 %
|
|
lvl 2
|
17 %
|
17 %
|
17 %
|
21 %
|
24 %
|
|
lvl 3
|
1 %
|
1 %
|
2 %
|
3 %
|
4 %
|
|
age 40-44
|
|
lvl 1
|
82 %
|
80 %
|
78 %
|
75 %
|
69 %
|
|
lvl 2
|
16 %
|
19 %
|
20 %
|
23 %
|
26 %
|
|
lvl 3
|
1 %
|
2 %
|
2 %
|
2 %
|
6 %
|
|
age 45-49
|
|
lvl 1
|
81 %
|
81 %
|
77 %
|
75 %
|
69 %
|
|
lvl 2
|
18 %
|
18 %
|
20 %
|
23 %
|
25 %
|
|
lvl 3
|
1 %
|
1 %
|
3 %
|
2 %
|
6 %
|
|
age 50-54
|
|
lvl 1
|
80 %
|
81 %
|
73 %
|
69 %
|
66 %
|
|
lvl 2
|
19 %
|
18 %
|
24 %
|
27 %
|
28 %
|
|
lvl 3
|
1 %
|
1 %
|
3 %
|
4 %
|
6 %
|
|
age 55-59
|
|
lvl 1
|
80 %
|
79 %
|
75 %
|
70 %
|
61 %
|
|
lvl 2
|
19 %
|
19 %
|
22 %
|
25 %
|
32 %
|
|
lvl 3
|
1 %
|
2 %
|
3 %
|
5 %
|
7 %
|
|
age 60-64
|
|
lvl 1
|
81 %
|
79 %
|
75 %
|
76 %
|
66 %
|
|
lvl 2
|
18 %
|
20 %
|
23 %
|
22 %
|
29 %
|
|
lvl 3
|
1 %
|
1 %
|
2 %
|
2 %
|
5 %
|
|
age 65-69
|
|
lvl 1
|
83 %
|
78 %
|
80 %
|
75 %
|
68 %
|
|
lvl 2
|
17 %
|
20 %
|
18 %
|
23 %
|
28 %
|
|
lvl 3
|
1 %
|
2 %
|
2 %
|
2 %
|
4 %
|
|
age 70-74
|
|
lvl 1
|
80 %
|
81 %
|
78 %
|
75 %
|
71 %
|
|
lvl 2
|
19 %
|
18 %
|
20 %
|
24 %
|
26 %
|
|
lvl 3
|
1 %
|
1 %
|
2 %
|
2 %
|
4 %
|
|
age 75-79
|
|
lvl 1
|
80 %
|
78 %
|
77 %
|
76 %
|
68 %
|
|
lvl 2
|
19 %
|
21 %
|
21 %
|
22 %
|
29 %
|
|
lvl 3
|
1 %
|
1 %
|
2 %
|
1 %
|
3 %
|
|
age 80-84
|
|
lvl 1
|
76 %
|
76 %
|
73 %
|
74 %
|
72 %
|
|
lvl 2
|
24 %
|
23 %
|
26 %
|
23 %
|
27 %
|
|
lvl 3
|
1 %
|
1 %
|
1 %
|
3 %
|
1 %
|
|
age 85-89
|
|
lvl 1
|
69 %
|
75 %
|
72 %
|
67 %
|
65 %
|
|
lvl 2
|
29 %
|
25 %
|
25 %
|
31 %
|
31 %
|
|
lvl 3
|
2 %
|
0 %
|
3 %
|
2 %
|
4 %
|
|
age 90+
|
|
lvl 1
|
73 %
|
72 %
|
69 %
|
70 %
|
70 %
|
|
lvl 2
|
24 %
|
26 %
|
31 %
|
30 %
|
30 %
|
|
lvl 3
|
3 %
|
3 %
|
0 %
|
0 %
|
0 %
|
Mobility - Male
|
level
|
Least deprived
|
Less deprived
|
Median deprived
|
More deprieved
|
Most deprived
|
|
age 16-17?
|
|
lvl 1
|
96 %
|
96 %
|
98 %
|
98 %
|
99 %
|
|
lvl 2
|
4 %
|
4 %
|
2 %
|
2 %
|
1 %
|
|
lvl 3
|
1 %
|
0 %
|
0 %
|
0 %
|
0 %
|
|
age 18-20?
|
|
lvl 1
|
97 %
|
97 %
|
97 %
|
98 %
|
91 %
|
|
lvl 2
|
3 %
|
3 %
|
3 %
|
2 %
|
9 %
|
|
lvl 3
|
0 %
|
0 %
|
0 %
|
0 %
|
0 %
|
|
age 20-24
|
|
lvl 1
|
97 %
|
97 %
|
95 %
|
96 %
|
94 %
|
|
lvl 2
|
3 %
|
3 %
|
5 %
|
4 %
|
6 %
|
|
lvl 3
|
0 %
|
0 %
|
0 %
|
0 %
|
0 %
|
|
age 25-29
|
|
lvl 1
|
96 %
|
96 %
|
97 %
|
94 %
|
94 %
|
|
lvl 2
|
4 %
|
4 %
|
3 %
|
6 %
|
5 %
|
|
lvl 3
|
0 %
|
0 %
|
0 %
|
0 %
|
0 %
|
|
age 30-34
|
|
lvl 1
|
97 %
|
96 %
|
95 %
|
93 %
|
91 %
|
|
lvl 2
|
3 %
|
4 %
|
5 %
|
7 %
|
9 %
|
|
lvl 3
|
0 %
|
0 %
|
0 %
|
0 %
|
0 %
|
|
age 35-39
|
|
lvl 1
|
96 %
|
93 %
|
93 %
|
91 %
|
87 %
|
|
lvl 2
|
4 %
|
7 %
|
6 %
|
9 %
|
13 %
|
|
lvl 3
|
0 %
|
0 %
|
0 %
|
0 %
|
0 %
|
|
age 40-44
|
|
lvl 1
|
95 %
|
93 %
|
91 %
|
89 %
|
82 %
|
|
lvl 2
|
5 %
|
7 %
|
9 %
|
11 %
|
18 %
|
|
lvl 3
|
0 %
|
0 %
|
0 %
|
0 %
|
1 %
|
|
age 45-49
|
|
lvl 1
|
92 %
|
91 %
|
88 %
|
84 %
|
75 %
|
|
lvl 2
|
8 %
|
9 %
|
12 %
|
15 %
|
25 %
|
|
lvl 3
|
0 %
|
0 %
|
0 %
|
0 %
|
0 %
|
|
age 50-54
|
|
lvl 1
|
89 %
|
86 %
|
81 %
|
76 %
|
67 %
|
|
lvl 2
|
11 %
|
14 %
|
19 %
|
24 %
|
32 %
|
|
lvl 3
|
0 %
|
0 %
|
0 %
|
0 %
|
1 %
|
|
age 55-59
|
|
lvl 1
|
86 %
|
81 %
|
77 %
|
73 %
|
63 %
|
|
lvl 2
|
14 %
|
19 %
|
23 %
|
27 %
|
37 %
|
|
lvl 3
|
0 %
|
0 %
|
0 %
|
0 %
|
0 %
|
|
age 60-64
|
|
lvl 1
|
83 %
|
79 %
|
75 %
|
70 %
|
60 %
|
|
lvl 2
|
17 %
|
21 %
|
25 %
|
30 %
|
40 %
|
|
lvl 3
|
0 %
|
0 %
|
0 %
|
0 %
|
0 %
|
|
age 65-69
|
|
lvl 1
|
78 %
|
76 %
|
72 %
|
63 %
|
58 %
|
|
lvl 2
|
22 %
|
24 %
|
28 %
|
36 %
|
42 %
|
|
lvl 3
|
0 %
|
0 %
|
0 %
|
0 %
|
0 %
|
|
age 70-74
|
|
lvl 1
|
70 %
|
65 %
|
61 %
|
56 %
|
55 %
|
|
lvl 2
|
30 %
|
35 %
|
39 %
|
44 %
|
45 %
|
|
lvl 3
|
0 %
|
0 %
|
0 %
|
0 %
|
0 %
|
|
age 75-79
|
|
lvl 1
|
65 %
|
53 %
|
56 %
|
51 %
|
43 %
|
|
lvl 2
|
35 %
|
47 %
|
44 %
|
48 %
|
57 %
|
|
lvl 3
|
0 %
|
0 %
|
0 %
|
0 %
|
0 %
|
|
age 80-84
|
|
lvl 1
|
50 %
|
50 %
|
43 %
|
40 %
|
30 %
|
|
lvl 2
|
50 %
|
50 %
|
56 %
|
60 %
|
69 %
|
|
lvl 3
|
0 %
|
0 %
|
1 %
|
1 %
|
1 %
|
|
age 85-89
|
|
lvl 1
|
37 %
|
36 %
|
30 %
|
24 %
|
27 %
|
|
lvl 2
|
63 %
|
64 %
|
70 %
|
76 %
|
73 %
|
|
lvl 3
|
0 %
|
0 %
|
0 %
|
0 %
|
0 %
|
|
age 90+
|
|
lvl 1
|
31 %
|
21 %
|
24 %
|
31 %
|
11 %
|
|
lvl 2
|
69 %
|
77 %
|
74 %
|
67 %
|
89 %
|
|
lvl 3
|
0 %
|
1 %
|
1 %
|
2 %
|
0 %
|
Self-care - Male
|
level
|
Least deprived
|
Less deprived
|
Median deprived
|
More deprieved
|
Most deprived
|
|
age 16-17?
|
|
lvl 1
|
98 %
|
100 %
|
99 %
|
99 %
|
100 %
|
|
lvl 2
|
1 %
|
0 %
|
1 %
|
1 %
|
0 %
|
|
lvl 3
|
1 %
|
0 %
|
0 %
|
0 %
|
0 %
|
|
age 18-20?
|
|
lvl 1
|
100 %
|
99 %
|
100 %
|
98 %
|
100 %
|
|
lvl 2
|
0 %
|
1 %
|
0 %
|
2 %
|
0 %
|
|
lvl 3
|
0 %
|
0 %
|
0 %
|
0 %
|
0 %
|
|
age 20-24
|
|
lvl 1
|
100 %
|
100 %
|
99 %
|
99 %
|
98 %
|
|
lvl 2
|
0 %
|
0 %
|
1 %
|
1 %
|
1 %
|
|
lvl 3
|
0 %
|
0 %
|
0 %
|
0 %
|
0 %
|
|
age 25-29
|
|
lvl 1
|
99 %
|
99 %
|
99 %
|
99 %
|
98 %
|
|
lvl 2
|
1 %
|
1 %
|
1 %
|
1 %
|
2 %
|
|
lvl 3
|
0 %
|
0 %
|
0 %
|
0 %
|
0 %
|
|
age 30-34
|
|
lvl 1
|
100 %
|
98 %
|
99 %
|
98 %
|
97 %
|
|
lvl 2
|
0 %
|
1 %
|
1 %
|
2 %
|
2 %
|
|
lvl 3
|
0 %
|
0 %
|
0 %
|
0 %
|
0 %
|
|
age 35-39
|
|
lvl 1
|
99 %
|
98 %
|
98 %
|
97 %
|
96 %
|
|
lvl 2
|
1 %
|
1 %
|
1 %
|
3 %
|
4 %
|
|
lvl 3
|
0 %
|
0 %
|
0 %
|
0 %
|
0 %
|
|
age 40-44
|
|
lvl 1
|
99 %
|
98 %
|
98 %
|
96 %
|
93 %
|
|
lvl 2
|
1 %
|
2 %
|
2 %
|
4 %
|
7 %
|
|
lvl 3
|
0 %
|
0 %
|
0 %
|
0 %
|
0 %
|
|
age 45-49
|
|
lvl 1
|
99 %
|
98 %
|
96 %
|
95 %
|
91 %
|
|
lvl 2
|
1 %
|
2 %
|
4 %
|
4 %
|
8 %
|
|
lvl 3
|
0 %
|
0 %
|
0 %
|
0 %
|
1 %
|
|
age 50-54
|
|
lvl 1
|
98 %
|
96 %
|
95 %
|
92 %
|
86 %
|
|
lvl 2
|
2 %
|
4 %
|
5 %
|
8 %
|
13 %
|
|
lvl 3
|
0 %
|
0 %
|
1 %
|
0 %
|
1 %
|
|
age 55-59
|
|
lvl 1
|
96 %
|
96 %
|
93 %
|
89 %
|
85 %
|
|
lvl 2
|
4 %
|
4 %
|
7 %
|
10 %
|
14 %
|
|
lvl 3
|
0 %
|
0 %
|
0 %
|
0 %
|
1 %
|
|
age 60-64
|
|
lvl 1
|
97 %
|
95 %
|
92 %
|
92 %
|
85 %
|
|
lvl 2
|
3 %
|
4 %
|
7 %
|
8 %
|
14 %
|
|
lvl 3
|
0 %
|
0 %
|
0 %
|
0 %
|
1 %
|
|
age 65-69
|
|
lvl 1
|
97 %
|
93 %
|
93 %
|
90 %
|
86 %
|
|
lvl 2
|
3 %
|
6 %
|
7 %
|
10 %
|
13 %
|
|
lvl 3
|
0 %
|
0 %
|
0 %
|
1 %
|
1 %
|
|
age 70-74
|
|
lvl 1
|
94 %
|
91 %
|
93 %
|
87 %
|
86 %
|
|
lvl 2
|
6 %
|
9 %
|
7 %
|
12 %
|
13 %
|
|
lvl 3
|
0 %
|
0 %
|
0 %
|
1 %
|
1 %
|
|
age 75-79
|
|
lvl 1
|
93 %
|
88 %
|
89 %
|
89 %
|
78 %
|
|
lvl 2
|
6 %
|
12 %
|
10 %
|
10 %
|
22 %
|
|
lvl 3
|
0 %
|
0 %
|
1 %
|
1 %
|
0 %
|
|
age 80-84
|
|
lvl 1
|
86 %
|
88 %
|
81 %
|
85 %
|
78 %
|
|
lvl 2
|
13 %
|
11 %
|
18 %
|
13 %
|
21 %
|
|
lvl 3
|
1 %
|
1 %
|
2 %
|
1 %
|
1 %
|
|
age 85-89
|
|
lvl 1
|
82 %
|
78 %
|
79 %
|
75 %
|
66 %
|
|
lvl 2
|
17 %
|
22 %
|
19 %
|
23 %
|
32 %
|
|
lvl 3
|
2 %
|
1 %
|
2 %
|
2 %
|
3 %
|
|
age 90+
|
|
lvl 1
|
70 %
|
68 %
|
66 %
|
68 %
|
57 %
|
|
lvl 2
|
28 %
|
28 %
|
28 %
|
27 %
|
41 %
|
|
lvl 3
|
1 %
|
4 %
|
6 %
|
5 %
|
3 %
|
Usual activities - Male
|
level
|
Least deprived
|
Less deprived
|
Median deprived
|
More deprieved
|
Most deprived
|
|
age 16-17?
|
|
lvl 1
|
95 %
|
97 %
|
97 %
|
97 %
|
97 %
|
|
lvl 2
|
4 %
|
3 %
|
3 %
|
3 %
|
3 %
|
|
lvl 3
|
1 %
|
0 %
|
0 %
|
1 %
|
0 %
|
|
age 18-20?
|
|
lvl 1
|
97 %
|
94 %
|
97 %
|
96 %
|
90 %
|
|
lvl 2
|
3 %
|
6 %
|
3 %
|
4 %
|
9 %
|
|
lvl 3
|
0 %
|
0 %
|
0 %
|
0 %
|
0 %
|
|
age 20-24
|
|
lvl 1
|
95 %
|
95 %
|
91 %
|
95 %
|
92 %
|
|
lvl 2
|
5 %
|
5 %
|
8 %
|
5 %
|
7 %
|
|
lvl 3
|
0 %
|
0 %
|
0 %
|
0 %
|
1 %
|
|
age 25-29
|
|
lvl 1
|
94 %
|
93 %
|
93 %
|
93 %
|
90 %
|
|
lvl 2
|
5 %
|
7 %
|
6 %
|
7 %
|
10 %
|
|
lvl 3
|
1 %
|
0 %
|
0 %
|
0 %
|
0 %
|
|
age 30-34
|
|
lvl 1
|
93 %
|
92 %
|
93 %
|
91 %
|
90 %
|
|
lvl 2
|
7 %
|
7 %
|
6 %
|
8 %
|
9 %
|
|
lvl 3
|
0 %
|
0 %
|
0 %
|
1 %
|
1 %
|
|
age 35-39
|
|
lvl 1
|
93 %
|
92 %
|
91 %
|
89 %
|
86 %
|
|
lvl 2
|
7 %
|
8 %
|
8 %
|
11 %
|
13 %
|
|
lvl 3
|
0 %
|
0 %
|
1 %
|
1 %
|
1 %
|
|
age 40-44
|
|
lvl 1
|
92 %
|
92 %
|
89 %
|
86 %
|
82 %
|
|
lvl 2
|
7 %
|
7 %
|
11 %
|
13 %
|
17 %
|
|
lvl 3
|
1 %
|
1 %
|
0 %
|
1 %
|
2 %
|
|
age 45-49
|
|
lvl 1
|
91 %
|
89 %
|
88 %
|
83 %
|
76 %
|
|
lvl 2
|
9 %
|
10 %
|
11 %
|
16 %
|
22 %
|
|
lvl 3
|
0 %
|
1 %
|
2 %
|
1 %
|
2 %
|
|
age 50-54
|
|
lvl 1
|
90 %
|
85 %
|
81 %
|
76 %
|
68 %
|
|
lvl 2
|
10 %
|
13 %
|
16 %
|
21 %
|
28 %
|
|
lvl 3
|
0 %
|
2 %
|
3 %
|
3 %
|
4 %
|
|
age 55-59
|
|
lvl 1
|
86 %
|
82 %
|
78 %
|
75 %
|
65 %
|
|
lvl 2
|
13 %
|
17 %
|
20 %
|
22 %
|
31 %
|
|
lvl 3
|
1 %
|
1 %
|
2 %
|
3 %
|
4 %
|
|
age 60-64
|
|
lvl 1
|
85 %
|
81 %
|
77 %
|
74 %
|
66 %
|
|
lvl 2
|
14 %
|
17 %
|
20 %
|
24 %
|
31 %
|
|
lvl 3
|
1 %
|
1 %
|
2 %
|
2 %
|
4 %
|
|
age 65-69
|
|
lvl 1
|
84 %
|
80 %
|
80 %
|
71 %
|
66 %
|
|
lvl 2
|
15 %
|
18 %
|
19 %
|
27 %
|
29 %
|
|
lvl 3
|
1 %
|
2 %
|
2 %
|
2 %
|
5 %
|
|
age 70-74
|
|
lvl 1
|
76 %
|
73 %
|
71 %
|
65 %
|
60 %
|
|
lvl 2
|
22 %
|
26 %
|
27 %
|
32 %
|
36 %
|
|
lvl 3
|
1 %
|
2 %
|
2 %
|
3 %
|
5 %
|
|
age 75-79
|
|
lvl 1
|
74 %
|
67 %
|
64 %
|
63 %
|
51 %
|
|
lvl 2
|
23 %
|
31 %
|
31 %
|
31 %
|
42 %
|
|
lvl 3
|
2 %
|
3 %
|
4 %
|
6 %
|
7 %
|
|
age 80-84
|
|
lvl 1
|
57 %
|
65 %
|
56 %
|
49 %
|
47 %
|
|
lvl 2
|
37 %
|
31 %
|
38 %
|
43 %
|
43 %
|
|
lvl 3
|
6 %
|
4 %
|
6 %
|
7 %
|
9 %
|
|
age 85-89
|
|
lvl 1
|
46 %
|
44 %
|
43 %
|
35 %
|
36 %
|
|
lvl 2
|
47 %
|
48 %
|
48 %
|
56 %
|
50 %
|
|
lvl 3
|
7 %
|
8 %
|
9 %
|
9 %
|
14 %
|
|
age 90+
|
|
lvl 1
|
42 %
|
39 %
|
36 %
|
40 %
|
22 %
|
|
lvl 2
|
47 %
|
52 %
|
46 %
|
47 %
|
56 %
|
|
lvl 3
|
11 %
|
9 %
|
18 %
|
13 %
|
22 %
|
Pain/Discomfort - Male
|
level
|
Least deprived
|
Less deprived
|
Median deprived
|
More deprieved
|
Most deprived
|
|
age 16-17?
|
|
lvl 1
|
87 %
|
92 %
|
83 %
|
86 %
|
84 %
|
|
lvl 2
|
13 %
|
8 %
|
17 %
|
14 %
|
15 %
|
|
lvl 3
|
1 %
|
0 %
|
0 %
|
0 %
|
1 %
|
|
age 18-20?
|
|
lvl 1
|
83 %
|
84 %
|
85 %
|
87 %
|
79 %
|
|
lvl 2
|
17 %
|
16 %
|
14 %
|
12 %
|
19 %
|
|
lvl 3
|
0 %
|
0 %
|
0 %
|
0 %
|
2 %
|
|
age 20-24
|
|
lvl 1
|
84 %
|
88 %
|
84 %
|
85 %
|
82 %
|
|
lvl 2
|
15 %
|
12 %
|
14 %
|
14 %
|
17 %
|
|
lvl 3
|
0 %
|
0 %
|
2 %
|
2 %
|
1 %
|
|
age 25-29
|
|
lvl 1
|
84 %
|
84 %
|
87 %
|
83 %
|
81 %
|
|
lvl 2
|
15 %
|
16 %
|
13 %
|
15 %
|
18 %
|
|
lvl 3
|
1 %
|
1 %
|
1 %
|
2 %
|
1 %
|
|
age 30-34
|
|
lvl 1
|
86 %
|
85 %
|
80 %
|
80 %
|
76 %
|
|
lvl 2
|
13 %
|
14 %
|
19 %
|
18 %
|
21 %
|
|
lvl 3
|
1 %
|
1 %
|
1 %
|
2 %
|
4 %
|
|
age 35-39
|
|
lvl 1
|
84 %
|
78 %
|
78 %
|
74 %
|
69 %
|
|
lvl 2
|
16 %
|
20 %
|
20 %
|
24 %
|
26 %
|
|
lvl 3
|
1 %
|
2 %
|
2 %
|
2 %
|
4 %
|
|
age 40-44
|
|
lvl 1
|
79 %
|
77 %
|
74 %
|
69 %
|
63 %
|
|
lvl 2
|
19 %
|
20 %
|
24 %
|
27 %
|
31 %
|
|
lvl 3
|
1 %
|
2 %
|
2 %
|
3 %
|
6 %
|
|
age 45-49
|
|
lvl 1
|
74 %
|
72 %
|
70 %
|
65 %
|
55 %
|
|
lvl 2
|
25 %
|
26 %
|
27 %
|
30 %
|
38 %
|
|
lvl 3
|
2 %
|
2 %
|
3 %
|
4 %
|
7 %
|
|
age 50-54
|
|
lvl 1
|
68 %
|
65 %
|
56 %
|
53 %
|
47 %
|
|
lvl 2
|
30 %
|
33 %
|
39 %
|
40 %
|
41 %
|
|
lvl 3
|
2 %
|
3 %
|
5 %
|
7 %
|
12 %
|
|
age 55-59
|
|
lvl 1
|
66 %
|
60 %
|
55 %
|
52 %
|
41 %
|
|
lvl 2
|
31 %
|
35 %
|
38 %
|
38 %
|
43 %
|
|
lvl 3
|
3 %
|
4 %
|
6 %
|
9 %
|
16 %
|
|
age 60-64
|
|
lvl 1
|
60 %
|
57 %
|
50 %
|
47 %
|
39 %
|
|
lvl 2
|
37 %
|
39 %
|
43 %
|
44 %
|
48 %
|
|
lvl 3
|
2 %
|
5 %
|
7 %
|
8 %
|
13 %
|
|
age 65-69
|
|
lvl 1
|
58 %
|
54 %
|
49 %
|
45 %
|
38 %
|
|
lvl 2
|
38 %
|
40 %
|
46 %
|
45 %
|
49 %
|
|
lvl 3
|
4 %
|
6 %
|
5 %
|
9 %
|
13 %
|
|
age 70-74
|
|
lvl 1
|
49 %
|
44 %
|
43 %
|
37 %
|
34 %
|
|
lvl 2
|
47 %
|
50 %
|
50 %
|
53 %
|
52 %
|
|
lvl 3
|
4 %
|
6 %
|
7 %
|
10 %
|
14 %
|
|
age 75-79
|
|
lvl 1
|
48 %
|
37 %
|
39 %
|
38 %
|
29 %
|
|
lvl 2
|
48 %
|
54 %
|
54 %
|
54 %
|
57 %
|
|
lvl 3
|
4 %
|
9 %
|
7 %
|
8 %
|
14 %
|
|
age 80-84
|
|
lvl 1
|
37 %
|
34 %
|
36 %
|
36 %
|
30 %
|
|
lvl 2
|
53 %
|
57 %
|
55 %
|
53 %
|
56 %
|
|
lvl 3
|
10 %
|
9 %
|
9 %
|
11 %
|
14 %
|
|
age 85-89
|
|
lvl 1
|
34 %
|
31 %
|
28 %
|
28 %
|
32 %
|
|
lvl 2
|
57 %
|
62 %
|
57 %
|
58 %
|
52 %
|
|
lvl 3
|
8 %
|
7 %
|
15 %
|
14 %
|
17 %
|
|
age 90+
|
|
lvl 1
|
37 %
|
39 %
|
36 %
|
44 %
|
26 %
|
|
lvl 2
|
53 %
|
50 %
|
57 %
|
44 %
|
56 %
|
|
lvl 3
|
10 %
|
11 %
|
8 %
|
12 %
|
18 %
|
Anxiety/Depression - Male
|
level
|
Least deprived
|
Less deprived
|
Median deprived
|
More deprieved
|
Most deprived
|
|
age 16-17?
|
|
lvl 1
|
83 %
|
86 %
|
83 %
|
84 %
|
87 %
|
|
lvl 2
|
16 %
|
13 %
|
17 %
|
16 %
|
12 %
|
|
lvl 3
|
1 %
|
1 %
|
0 %
|
1 %
|
1 %
|
|
age 18-20?
|
|
lvl 1
|
83 %
|
79 %
|
86 %
|
85 %
|
84 %
|
|
lvl 2
|
16 %
|
20 %
|
12 %
|
14 %
|
12 %
|
|
lvl 3
|
1 %
|
1 %
|
3 %
|
1 %
|
4 %
|
|
age 20-24
|
|
lvl 1
|
80 %
|
81 %
|
80 %
|
80 %
|
79 %
|
|
lvl 2
|
18 %
|
18 %
|
19 %
|
19 %
|
19 %
|
|
lvl 3
|
2 %
|
1 %
|
1 %
|
1 %
|
2 %
|
|
age 25-29
|
|
lvl 1
|
85 %
|
82 %
|
81 %
|
80 %
|
79 %
|
|
lvl 2
|
14 %
|
16 %
|
18 %
|
18 %
|
18 %
|
|
lvl 3
|
1 %
|
1 %
|
1 %
|
2 %
|
3 %
|
|
age 30-34
|
|
lvl 1
|
85 %
|
85 %
|
82 %
|
80 %
|
76 %
|
|
lvl 2
|
14 %
|
15 %
|
16 %
|
18 %
|
21 %
|
|
lvl 3
|
1 %
|
0 %
|
2 %
|
2 %
|
3 %
|
|
age 35-39
|
|
lvl 1
|
82 %
|
82 %
|
81 %
|
77 %
|
72 %
|
|
lvl 2
|
17 %
|
17 %
|
17 %
|
21 %
|
24 %
|
|
lvl 3
|
1 %
|
1 %
|
2 %
|
3 %
|
4 %
|
|
age 40-44
|
|
lvl 1
|
82 %
|
80 %
|
78 %
|
75 %
|
69 %
|
|
lvl 2
|
16 %
|
19 %
|
20 %
|
23 %
|
26 %
|
|
lvl 3
|
1 %
|
2 %
|
2 %
|
2 %
|
6 %
|
|
age 45-49
|
|
lvl 1
|
81 %
|
81 %
|
77 %
|
75 %
|
69 %
|
|
lvl 2
|
18 %
|
18 %
|
20 %
|
23 %
|
25 %
|
|
lvl 3
|
1 %
|
1 %
|
3 %
|
2 %
|
6 %
|
|
age 50-54
|
|
lvl 1
|
80 %
|
81 %
|
73 %
|
69 %
|
66 %
|
|
lvl 2
|
19 %
|
18 %
|
24 %
|
27 %
|
28 %
|
|
lvl 3
|
1 %
|
1 %
|
3 %
|
4 %
|
6 %
|
|
age 55-59
|
|
lvl 1
|
80 %
|
79 %
|
75 %
|
70 %
|
61 %
|
|
lvl 2
|
19 %
|
19 %
|
22 %
|
25 %
|
32 %
|
|
lvl 3
|
1 %
|
2 %
|
3 %
|
5 %
|
7 %
|
|
age 60-64
|
|
lvl 1
|
81 %
|
79 %
|
75 %
|
76 %
|
66 %
|
|
lvl 2
|
18 %
|
20 %
|
23 %
|
22 %
|
29 %
|
|
lvl 3
|
1 %
|
1 %
|
2 %
|
2 %
|
5 %
|
|
age 65-69
|
|
lvl 1
|
83 %
|
78 %
|
80 %
|
75 %
|
68 %
|
|
lvl 2
|
17 %
|
20 %
|
18 %
|
23 %
|
28 %
|
|
lvl 3
|
1 %
|
2 %
|
2 %
|
2 %
|
4 %
|
|
age 70-74
|
|
lvl 1
|
80 %
|
81 %
|
78 %
|
75 %
|
71 %
|
|
lvl 2
|
19 %
|
18 %
|
20 %
|
24 %
|
26 %
|
|
lvl 3
|
1 %
|
1 %
|
2 %
|
2 %
|
4 %
|
|
age 75-79
|
|
lvl 1
|
80 %
|
78 %
|
77 %
|
76 %
|
68 %
|
|
lvl 2
|
19 %
|
21 %
|
21 %
|
22 %
|
29 %
|
|
lvl 3
|
1 %
|
1 %
|
2 %
|
1 %
|
3 %
|
|
age 80-84
|
|
lvl 1
|
76 %
|
76 %
|
73 %
|
74 %
|
72 %
|
|
lvl 2
|
24 %
|
23 %
|
26 %
|
23 %
|
27 %
|
|
lvl 3
|
1 %
|
1 %
|
1 %
|
3 %
|
1 %
|
|
age 85-89
|
|
lvl 1
|
69 %
|
75 %
|
72 %
|
67 %
|
65 %
|
|
lvl 2
|
29 %
|
25 %
|
25 %
|
31 %
|
31 %
|
|
lvl 3
|
2 %
|
0 %
|
3 %
|
2 %
|
4 %
|
|
age 90+
|
|
lvl 1
|
73 %
|
72 %
|
69 %
|
70 %
|
70 %
|
|
lvl 2
|
24 %
|
26 %
|
31 %
|
30 %
|
30 %
|
|
lvl 3
|
3 %
|
3 %
|
0 %
|
0 %
|
0 %
|
fin.